Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping

Author:

Apthorpe Noah1,Huang Danny Yuxing1,Reisman Dillon1,Narayanan Arvind1,Feamster Nick1

Affiliation:

1. Princeton University , Department of Computer Science

Abstract

Abstract The proliferation of smart home Internet of things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider) can infer private in-home activities by analyzing Internet traffic from commercially available smart home devices even when the devices use end-to-end transport-layer encryption. We evaluate common approaches for defending against these types of traffic analysis attacks, including firewalls, virtual private networks, and independent link padding, and find that none sufficiently conceal user activities with reasonable data overhead. We develop a new defense, “stochastic traffic padding” (STP), that makes it difficult for a passive network adversary to reliably distinguish genuine user activities from generated traffic patterns designed to look like user interactions. Our analysis provides a theoretical bound on an adversary’s ability to accurately detect genuine user activities as a function of the amount of additional cover traffic generated by the defense technique.

Publisher

Walter de Gruyter GmbH

Subject

General Medicine

Reference46 articles.

1. [1] Antonakakis, M., April, T., Bailey, M., Bernhard, M., Bursztein, E., Cochran, J., Durumeric, Z., Halderman, J. A., Invernizzi, L., Kallitsis, M., et al. Understanding the Mirai botnet. In 26th USENIX Security Symposium (USENIX Security 17) (2017), pp. 1092–1110.

2. [2] Apthorpe, N., Reisman, D., and Feamster, N. Closing the blinds: Four strategies for protecting smart home privacy from network observers. Workshop on Technology and Consumer Protection (ConPro) (2017).

3. [3] Apthorpe, N., Reisman, D., and Feamster, N. A smart home is no castle: Privacy vulnerabilities of encrypted IoT traffic. Data and Algorithmic Transparency Workshop (DAT) (2017).

4. [4] Apthorpe, N., Reisman, D., Sundaresan, S., Narayanan, A., and Feamster, N. Spying on the smart home: Privacy attacks and defenses on encrypted IoT traffic. arXiv preprint arXiv:1708.05044 (2017).

5. [5] Back, A., Möller, U., and Stiglic, A. Traffic analysis attacks and trade-offs in anonymity providing systems. In International Workshop on Information Hiding (2001), Springer, pp. 245–257.

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